In some cases, models may be used to model one or more factors. For example, a model may be created using software that generates a modeled value for each factor. The collection of modeled values may be referred to as a model dataset.
Because models are imperfect, a modeled value for a factor may in some instances deviate from a value for that factor in reality. As a result, the model may similarly deviate from reality. Such deviation of the model may be referred to as “model drift.” Model drift may pose a danger that the model will not accurately model reality. Danger associated with model drift may be referred to as “model risk.” The extent to which a single factor and/or its associated modeled value contributes to the model risk may be referred to as a “factor risk.”
In some instances, it may be desirable to dynamically isolate, identify, and illustrate factors that contribute to model drift and/or model risk. Existing technologies, however, which rely on static graphical user interfaces, do not provide such dynamic isolation, identification, and illustration of factors that contribute to model drift and/or model risk.